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Using Reflectance Sensors in Agronomy and Weed Science

Published online by Cambridge University Press:  20 January 2017

Warwick L. Felton ,
Clair L. Alston ,
Bruce M. Haigh ,
Paul G. Nash ,
Gail A. Wicks  and
Gordon E. Hanson
Warwick L. Felton
Affiliation:
NSW Agriculture, Centre for Crop Improvement, Tamworth, NSW 2340, Australia
Clair L. Alston
Affiliation:
NSW Agriculture, Centre for Crop Improvement, Tamworth, NSW 2340, Australia
Bruce M. Haigh
Affiliation:
NSW Agriculture, Centre for Crop Improvement, Tamworth, NSW 2340, Australia
Paul G. Nash
Affiliation:
NSW Agriculture, Centre for Crop Improvement, Tamworth, NSW 2340, Australia
Gail A. Wicks*
Affiliation:
West Central Research and Extension Center, University of Nebraska, North Platte, NE 69101
Gordon E. Hanson
Affiliation:
West Central Research and Extension Center, University of Nebraska, North Platte, NE 69101
*
Corresponding author's E-mail: [email protected]
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Abstract

Weed-detecting reflectance sensors were modified to allow selective interrogation of the near infrared–red ratio to estimate differences in plant biomass. Sampling was programmed to correspond to the forward movement of the field of view of the sensors. There was a linear relationship (r 2 > 0.80) between actual biomass and crop canopy analyzer (CCA) values up to 2,000 kg/ha for winter wheat sequentially thinned to create different amounts of biomass and up to 1,000 kg/ha for spring wheat sampled at different stages of development. At higher amounts of biomass the sensors underestimated the actual biomass. A linear relationship (r 2 = 0.73) was obtained with the CCA for the biomass of 76 chickpea cultivars at 500 growing degree days (GDD500). The reflectance sensors were used to determine differences in the herbicide response of soybean cultivars sprayed with increasing rates of herbicides. The CCA data resulted in better dose–response relationships than did biomass data for bromoxynil at 0.8 kg ai/ha and glyphosate at 1.35 kg ai/ha. There was no phytotoxicity to soybean with imazethapyr at 1.44 kg ai/ha. The method offers a quick and nondestructive means to measure differences in early-season crop growth. It also has potential in selecting crop cultivars with greater seedling vigor, as an indicator of crop nutrient status, in plant disease assessment, in determining crop cultivar responses to increasing herbicide dose rates, in weed mapping, and in studying temporal changes in crop or weed biomass.

Keywords

Bromoxynilglyphosateimazethapyrchickpea, Cicer arietinum L.soybean, Glycine max (L.) Merrwheat, Triticum aestivum L.Crop canopy analysiscrop growthnear infrared–redweed detectionCCA, crop canopy analyzerDAP, days after plantingDAT, days after treatmentFOV, field of viewGDD, growing degree daysLAI, leaf area indexNDVI, normalized vegetation indexNIR, near infraredR, red
Type
Research
Information
Weed Technology , Volume 16 , Issue 3 , September 2002 , pp. 520 - 527
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Adams, M. L., Norvell, W. A., Philpot, W. D., and Peverly, J. H. 2000. Spectral detection of micronutrient deficiency in ‘Bragg’ soybean. Agron. J. 92: 261–268.Google Scholar
Adcock, T. E., Forrest, W. N., and Banks, P. A. 1990. Measuring herbicide injury in soybeans (Glycine max) using a radiometer. Weed Sci. 38: 625–627.CrossRefGoogle Scholar
Aparicio, N., Villegas, D., Casadesus, J., Araus, J. L., and Royo, C. 2000. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agron. J. 92: 83–91.CrossRefGoogle Scholar
Beck, J. 1997. Reduced herbicide using photoelectronic detection. Proc. West. Soc. Weed Sci. 50: 102–107.Google Scholar
Donald, W. W. 1998. Estimating soybean (Glycine max) yield loss from herbicide damage using ground cover or rated stunting. Weed Sci. 46: 454–458.CrossRefGoogle Scholar
Felton, W. L. 1990. Use of weed detection for fallow weed control. In Proceedings of the Great Plains Conservation Tillage Symposium; August 21–24, 1990; Bismarck, ND. pp. 241–244.Google Scholar
Felton, W. L., Doss, A. F., Nash, P. G., and McCloy, K. R. 1991. A microprocessor controlled technology to selectively spot-spray weeds. Proceedings of the Symposium, Automated Agriculture in the 21st Century; December 16–17, 1991; Chicago. pp. 427–432.Google Scholar
Gilmour, A. R., Cullis, B. R., Welham, S. J., and Thompson, R. 1999. ASREML Reference Manual. NSW Agriculture Biometric Bulletin No. 3. NSW Agriculture. 234 p.Google Scholar
Grant, L. 1987. Review article. Diffuse and specular characteristics of leaf reflectance. Remote Sens. Environ. 22: 309–322.CrossRefGoogle Scholar
Haggar, R. J., Stent, C. J., and Isaac, S. 1983. A prototype hand-held patch sprayer for killing weeds, activated by spectral differences in crop/weed canopies. J. Agric. Eng. Res. 28: 349–358.CrossRefGoogle Scholar
Hastie, T. J. and Tibshirani, R. J. 1990. Generalized Additive Models. Chapter 2. Smoothing. London: Chapman and Hall. pp. 9–38.Google Scholar
Klingman, D. L. 1953. Effects of varying rates of 2,4-D and 2,4,5-T at different stages of growth on winter wheat. Agron. J. 45: 606–610.CrossRefGoogle Scholar
Kudsk, P. 1989. Experiences with reduced herbicide doses in Denmark and the development of factor-adjusted doses. Proc. Brighton Crop Prot. Conf.—Weeds. 545–553.Google Scholar
Lemerle, D., Fisher, J. A., and Hinkley, R. B. 1993. Radiometry accurately measures chlorsulfuron injury to barley. Aust. J. Agric. Res. 44: 13–21.CrossRefGoogle Scholar
Lemerle, D. and Hinkley, R. B. 1991. Tolerances of canola, field pea, lupin and faba bean cultivars to herbicides. Aust. J. Exp. Agric. 31: 379–386.CrossRefGoogle Scholar
Neeser, C., Martin, A. R., Juroszek, P., and Mortensen, D. A. 2000. A comparison of visual and photographic estimates of weed biomass and weed control. Weed Technol. 14: 586–590.CrossRefGoogle Scholar
Pinter, P. J. Jr. and Jackson, R. D. 1981. Dew and vapour pressure as complicating factors in the interpretation of spectral radiance from crops. In Proceedings of the 15th Symposium on Remote Sensing of the Environment. pp. 547–554.Google Scholar
Robison, L. R. and Fenster, C. R. 1973. Winter wheat response to herbicides applied postemergence. Agron. J. 65: 749–751.CrossRefGoogle Scholar
Streibig, J. C., Rudemo, M., and Jensen, J. E. 1993. Dose–response curves and statistical models. In Streibig, J. C. and Kudsk, P., eds. Herbicide Bioassays. Boca Raton, Fl: CRC Press. pp. 29–55.Google Scholar
Todd, L. A. and Kommedahl, T. 1994. Image analysis and visual estimates for evaluating disease reactions of corn to Fusarium stalk rot. Plant Dis. 78: 876–878.CrossRefGoogle Scholar